Accelerating Material Property Prediction using Generically Complete Isometry Invariants

arXiv:2401.15089v2 Announce Type: replace
Abstract: Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, …

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